tests.TestDataPortal.test_adjust_forward_fill_minute() - Code Metrics - Inspection of "Q 2.0" - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Pull Request — master (#858)

by Eddie

created 2015-12-17 19:26 UTC

test_adjust_forward_fill_minute() C

↳ Parent: tests.TestDataPortal

Complexity

Conditions

Size

Total Lines

149

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	7
dl	0
loc	149
rs	5.3043

How to fix Long Method

#
# Copyright 2015 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from datetime import timedelta
import bcolz
import numpy as np
import pandas as pd

from unittest import TestCase
from pandas.tslib import normalize_date
from testfixtures import TempDirectory
from zipline.data.data_portal import DataPortal
from zipline.data.us_equity_pricing import SQLiteAdjustmentWriter, \
    SQLiteAdjustmentReader
from zipline.finance.trading import TradingEnvironment, SimulationParameters
from zipline.data.us_equity_pricing import BcolzDailyBarReader
from zipline.data.us_equity_minutes import (
    MinuteBarWriterFromDataFrames,
    BcolzMinuteBarReader
)
from zipline.data.future_pricing import FutureMinuteReader
from .utils.daily_bar_writer import DailyBarWriterFromDataFrames


class TestDataPortal(TestCase):
    def test_forward_fill_minute(self):
        tempdir = TempDirectory()
        try:
            env = TradingEnvironment()
            env.write_data(
                equities_data={
                    0: {
                        'start_date': pd.Timestamp("2015-09-28", tz='UTC'),
                        'end_date': pd.Timestamp("2015-09-29", tz='UTC')
                        + timedelta(days=1)
                    }
                }
            )

            minutes = env.minutes_for_days_in_range(
                start=pd.Timestamp("2015-09-28", tz='UTC'),
                end=pd.Timestamp("2015-09-29", tz='UTC')
            )

            df = pd.DataFrame({
                # one missing bar, then 200 bars of real data,
                # then 1.5 days of missing data
                "open": np.array([0] + list(range(0, 200)) + [0] * 579)
                * 1000,
                "high": np.array([0] + list(range(1000, 1200)) + [0] * 579)
                * 1000,
                "low": np.array([0] + list(range(2000, 2200)) + [0] * 579)
                * 1000,
                "close": np.array([0] + list(range(3000, 3200)) + [0] * 579)
                * 1000,
                "volume": [0] + list(range(4000, 4200)) + [0] * 579,
                "minute": minutes
            })

            MinuteBarWriterFromDataFrames(
                pd.Timestamp('2002-01-02', tz='UTC')).write(
                    tempdir.path, {0: df})

            sim_params = SimulationParameters(
                period_start=minutes[0],
                period_end=minutes[-1],
                data_frequency="minute",
                env=env,
            )

            equity_minute_reader = BcolzMinuteBarReader(tempdir.path)

            dp = DataPortal(
                env,
                equity_minute_reader=equity_minute_reader,
            )

            for minute_idx, minute in enumerate(minutes):
                for field_idx, field in enumerate(
                        ["open", "high", "low", "close", "volume"]):
                    val = dp.get_spot_value(
                        0, field,
                        dt=minute,
                        data_frequency=sim_params.data_frequency)
                    if minute_idx == 0:
                        self.assertEqual(0, val)
                    elif minute_idx < 200:
                        self.assertEqual((minute_idx - 1) +
                                         (field_idx * 1000), val)
                    else:
                        self.assertEqual(199 + (field_idx * 1000), val)
        finally:
            tempdir.cleanup()

    def test_forward_fill_daily(self):
        tempdir = TempDirectory()
        try:
            # 17 trading days
            start_day = pd.Timestamp("2015-09-07", tz='UTC')
            end_day = pd.Timestamp("2015-09-30", tz='UTC')

            env = TradingEnvironment()
            env.write_data(
                equities_data={
                    0: {
                        'start_date': start_day,
                        'end_date': end_day
                    }
                }
            )

            days = env.days_in_range(start_day, end_day)

            # first bar is missing.  then 8 real bars.  then 8 more missing
            # bars.
            df = pd.DataFrame({
                "open": [0] + list(range(0, 8)) + [0] * 8,
                "high": [0] + list(range(10, 18)) + [0] * 8,
                "low": [0] + list(range(20, 28)) + [0] * 8,
                "close": [0] + list(range(30, 38)) + [0] * 8,
                "volume": [0] + list(range(40, 48)) + [0] * 8,
                "day": [day.value for day in days]
            }, index=days)

            assets = {0: df}
            path = os.path.join(tempdir.path, "testdaily.bcolz")

            DailyBarWriterFromDataFrames(assets).write(
                path,
                days,
                assets
            )

            sim_params = SimulationParameters(
                period_start=days[0],
                period_end=days[-1],
                data_frequency="daily"
            )

            equity_daily_reader = BcolzDailyBarReader(path)

            dp = DataPortal(
                env,
                equity_daily_reader=equity_daily_reader,
            )

            for day_idx, day in enumerate(days):
                for field_idx, field in enumerate(
                        ["open", "high", "low", "close", "volume"]):
                    val = dp.get_spot_value(
                        0, field,
                        dt=day,
                        data_frequency=sim_params.data_frequency)
                    if day_idx == 0:
                        self.assertEqual(0, val)
                    elif day_idx < 9:
                        self.assertEqual((day_idx - 1) + (field_idx * 10), val)
                    else:
                        self.assertEqual(7 + (field_idx * 10), val)
        finally:
            tempdir.cleanup()

    def test_adjust_forward_fill_minute(self):
        tempdir = TempDirectory()
        try:
            start_day = pd.Timestamp("2013-06-21", tz='UTC')
            end_day = pd.Timestamp("2013-06-24", tz='UTC')

            env = TradingEnvironment()
            env.write_data(
                equities_data={
                    0: {
                        'start_date': start_day,
                        'end_date': env.next_trading_day(end_day)
                    }
                }
            )

            minutes = env.minutes_for_days_in_range(
                start=start_day,
                end=end_day
            )

            df = pd.DataFrame({
                # 390 bars of real data, then 100 missing bars, then 290
                # bars of data again
                "open": np.array(list(range(0, 390)) + [0] * 100 +
                                 list(range(390, 680))) * 1000,
                "high": np.array(list(range(1000, 1390)) + [0] * 100 +
                                 list(range(1390, 1680))) * 1000,
                "low": np.array(list(range(2000, 2390)) + [0] * 100 +
                                list(range(2390, 2680))) * 1000,
                "close": np.array(list(range(3000, 3390)) + [0] * 100 +
                                  list(range(3390, 3680))) * 1000,
                "volume": np.array(list(range(4000, 4390)) + [0] * 100 +
                                   list(range(4390, 4680))),
                "minute": minutes
            })

            MinuteBarWriterFromDataFrames(
                pd.Timestamp('2002-01-02', tz='UTC')).write(
                    tempdir.path, {0: df})

            sim_params = SimulationParameters(
                period_start=minutes[0],
                period_end=minutes[-1],
                data_frequency="minute",
                env=env
            )

            # create a split for 6/24
            adjustments_path = os.path.join(tempdir.path, "adjustments.db")
            writer = SQLiteAdjustmentWriter(adjustments_path,
                                            pd.date_range(start=start_day,
                                                          end=end_day),
                                            None)

            splits = pd.DataFrame([{
                'effective_date': int(end_day.value / 1e9),
                'ratio': 0.5,
                'sid': 0
            }])

            dividend_data = {
                # Hackery to make the dtypes correct on an empty frame.
                'ex_date': np.array([], dtype='datetime64[ns]'),
                'pay_date': np.array([], dtype='datetime64[ns]'),
                'record_date': np.array([], dtype='datetime64[ns]'),
                'declared_date': np.array([], dtype='datetime64[ns]'),
                'amount': np.array([], dtype=float),
                'sid': np.array([], dtype=int),
            }
            dividends = pd.DataFrame(
                dividend_data,
                index=pd.DatetimeIndex([], tz='UTC'),
                columns=['ex_date',
                         'pay_date',
                         'record_date',
                         'declared_date',
                         'amount',
                         'sid']
            )

            merger_data = {
                # Hackery to make the dtypes correct on an empty frame.
                'effective_date': np.array([], dtype=int),
                'ratio': np.array([], dtype=float),
                'sid': np.array([], dtype=int),
            }
            mergers = pd.DataFrame(
                merger_data,
                index=pd.DatetimeIndex([], tz='UTC')
            )

            writer.write(splits, mergers, dividends)

            equity_minute_reader = BcolzMinuteBarReader(tempdir.path)

            dp = DataPortal(
                env,
                equity_minute_reader=equity_minute_reader,
                adjustment_reader=SQLiteAdjustmentReader(adjustments_path)
            )

            # phew, finally ready to start testing.
            for idx, minute in enumerate(minutes[:390]):
                for field_idx, field in enumerate(["open", "high", "low",
                                                   "close", "volume"]):
                    self.assertEqual(
                        dp.get_spot_value(
                            0, field,
                            dt=minute,
                            data_frequency=sim_params.data_frequency),
                        idx + (1000 * field_idx)
                    )

            for idx, minute in enumerate(minutes[390:490]):
                # no actual data for this part, so we'll forward-fill.
                # make sure the forward-filled values are adjusted.
                for field_idx, field in enumerate(["open", "high", "low",
                                                   "close"]):
                    self.assertEqual(
                        dp.get_spot_value(
                            0, field,
                            dt=minute,
                            data_frequency=sim_params.data_frequency),
                        (389 + (1000 * field_idx)) / 2.0
                    )

                self.assertEqual(
                    dp.get_spot_value(
                        0, "volume",
                        dt=minute,
                        data_frequency=sim_params.data_frequency),
                    8778  # 4389 * 2
                )

            for idx, minute in enumerate(minutes[490:]):
                # back to real data
                for field_idx, field in enumerate(["open", "high", "low",
                                                   "close", "volume"]):
                    self.assertEqual(
                        dp.get_spot_value(
                            0, field,
                            dt=minute,
                            data_frequency=sim_params.data_frequency
                        ),
                        (390 + idx + (1000 * field_idx))
                    )
        finally:
            tempdir.cleanup()

    def test_spot_value_futures(self):
        tempdir = TempDirectory()
        try:
            start_dt = pd.Timestamp("2015-11-20 20:11", tz='UTC')
            end_dt = pd.Timestamp(start_dt + timedelta(minutes=10000))

            zeroes_buffer = \
                [0] * int((start_dt -
                           normalize_date(start_dt)).total_seconds() / 60)

            df = pd.DataFrame({
                "open": np.array(zeroes_buffer + list(range(0, 10000))) * 1000,
                "high": np.array(
                    zeroes_buffer + list(range(10000, 20000))) * 1000,
                "low": np.array(
                    zeroes_buffer + list(range(20000, 30000))) * 1000,
                "close": np.array(
                    zeroes_buffer + list(range(30000, 40000))) * 1000,
                "volume": np.array(zeroes_buffer + list(range(40000, 50000)))
            })

            path = os.path.join(tempdir.path, "123.bcolz")
            ctable = bcolz.ctable.fromdataframe(df, rootdir=path)
            ctable.attrs["start_dt"] = start_dt.value / 1e9
            ctable.attrs["last_dt"] = end_dt.value / 1e9

            env = TradingEnvironment()
            env.write_data(futures_data={
                123: {
                    "start_date": normalize_date(start_dt),
                    "end_date": env.next_trading_day(normalize_date(end_dt)),
                    'symbol': 'TEST_FUTURE',
                    'asset_type': 'future',
                }
            })

            future_minute_reader = FutureMinuteReader(tempdir.path)

            dp = DataPortal(
                env,
                future_minute_reader=future_minute_reader
            )

            future123 = env.asset_finder.retrieve_asset(123)

            data_frequency = 'minute'

            for i in range(0, 10000):
                dt = pd.Timestamp(start_dt + timedelta(minutes=i))

                self.assertEqual(i,
                                 dp.get_spot_value(
                                     future123, "open", dt, data_frequency))
                self.assertEqual(i + 10000,
                                 dp.get_spot_value(
                                     future123, "high", dt, data_frequency))
                self.assertEqual(i + 20000,
                                 dp.get_spot_value(
                                     future123, "low", dt, data_frequency))
                self.assertEqual(i + 30000,
                                 dp.get_spot_value(
                                     future123, "close", dt, data_frequency))
                self.assertEqual(i + 40000,
                                 dp.get_spot_value(
                                     future123, "volume", dt, data_frequency))

        finally:
            tempdir.cleanup()


1			#
2			# Copyright 2015 Quantopian, Inc.
3			#
4			# Licensed under the Apache License, Version 2.0 (the "License");
5			# you may not use this file except in compliance with the License.
6			# You may obtain a copy of the License at
7			#
8			# http://www.apache.org/licenses/LICENSE-2.0
9			#
10			# Unless required by applicable law or agreed to in writing, software
11			# distributed under the License is distributed on an "AS IS" BASIS,
12			# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13			# See the License for the specific language governing permissions and
14			# limitations under the License.
15			import os
16			from datetime import timedelta
17			import bcolz
18			import numpy as np
19			import pandas as pd
20
21			from unittest import TestCase
22			from pandas.tslib import normalize_date
23			from testfixtures import TempDirectory
24			from zipline.data.data_portal import DataPortal
25			from zipline.data.us_equity_pricing import SQLiteAdjustmentWriter, \
26			SQLiteAdjustmentReader
27			from zipline.finance.trading import TradingEnvironment, SimulationParameters
28			from zipline.data.us_equity_pricing import BcolzDailyBarReader
29			from zipline.data.us_equity_minutes import (
30			MinuteBarWriterFromDataFrames,
31			BcolzMinuteBarReader
32			)
33			from zipline.data.future_pricing import FutureMinuteReader
34			from .utils.daily_bar_writer import DailyBarWriterFromDataFrames
35
36
37			class TestDataPortal(TestCase):
38			def test_forward_fill_minute(self):
39			tempdir = TempDirectory()
40			try:
41			env = TradingEnvironment()
42			env.write_data(
43			equities_data={
44			0: {
45			'start_date': pd.Timestamp("2015-09-28", tz='UTC'),
46			'end_date': pd.Timestamp("2015-09-29", tz='UTC')
47			+ timedelta(days=1)
48			}
49			}
50			)
51
52			minutes = env.minutes_for_days_in_range(
53			start=pd.Timestamp("2015-09-28", tz='UTC'),
54			end=pd.Timestamp("2015-09-29", tz='UTC')
55			)
56
57			df = pd.DataFrame({
58			# one missing bar, then 200 bars of real data,
59			# then 1.5 days of missing data
60			"open": np.array([0] + list(range(0, 200)) + [0] * 579)
61			* 1000,
62			"high": np.array([0] + list(range(1000, 1200)) + [0] * 579)
63			* 1000,
64			"low": np.array([0] + list(range(2000, 2200)) + [0] * 579)
65			* 1000,
66			"close": np.array([0] + list(range(3000, 3200)) + [0] * 579)
67			* 1000,
68			"volume": [0] + list(range(4000, 4200)) + [0] * 579,
69			"minute": minutes
70			})
71
72			MinuteBarWriterFromDataFrames(
73			pd.Timestamp('2002-01-02', tz='UTC')).write(
74			tempdir.path, {0: df})
75
76			sim_params = SimulationParameters(
77			period_start=minutes[0],
78			period_end=minutes[-1],
79			data_frequency="minute",
80			env=env,
81			)
82
83			equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
84
85			dp = DataPortal(
86			env,
87			equity_minute_reader=equity_minute_reader,
88			)
89
90			for minute_idx, minute in enumerate(minutes):
91			for field_idx, field in enumerate(
92			["open", "high", "low", "close", "volume"]):
93			val = dp.get_spot_value(
94			0, field,
95			dt=minute,
96			data_frequency=sim_params.data_frequency)
97			if minute_idx == 0:
98			self.assertEqual(0, val)
99			elif minute_idx < 200:
100			self.assertEqual((minute_idx - 1) +
101			(field_idx * 1000), val)
102			else:
103			self.assertEqual(199 + (field_idx * 1000), val)
104			finally:
105			tempdir.cleanup()
106
107			def test_forward_fill_daily(self):
108			tempdir = TempDirectory()
109			try:
110			# 17 trading days
111			start_day = pd.Timestamp("2015-09-07", tz='UTC')
112			end_day = pd.Timestamp("2015-09-30", tz='UTC')
113
114			env = TradingEnvironment()
115			env.write_data(
116			equities_data={
117			0: {
118			'start_date': start_day,
119			'end_date': end_day
120			}
121			}
122			)
123
124			days = env.days_in_range(start_day, end_day)
125
126			# first bar is missing. then 8 real bars. then 8 more missing
127			# bars.
128			df = pd.DataFrame({
129			"open": [0] + list(range(0, 8)) + [0] * 8,
130			"high": [0] + list(range(10, 18)) + [0] * 8,
131			"low": [0] + list(range(20, 28)) + [0] * 8,
132			"close": [0] + list(range(30, 38)) + [0] * 8,
133			"volume": [0] + list(range(40, 48)) + [0] * 8,
134			"day": [day.value for day in days]
135			}, index=days)
136
137			assets = {0: df}
138			path = os.path.join(tempdir.path, "testdaily.bcolz")
139
140			DailyBarWriterFromDataFrames(assets).write(
141			path,
142			days,
143			assets
144			)
145
146			sim_params = SimulationParameters(
147			period_start=days[0],
148			period_end=days[-1],
149			data_frequency="daily"
150			)
151
152			equity_daily_reader = BcolzDailyBarReader(path)
153
154			dp = DataPortal(
155			env,
156			equity_daily_reader=equity_daily_reader,
157			)
158
159			for day_idx, day in enumerate(days):
160			for field_idx, field in enumerate(
161			["open", "high", "low", "close", "volume"]):
162			val = dp.get_spot_value(
163			0, field,
164			dt=day,
165			data_frequency=sim_params.data_frequency)
166			if day_idx == 0:
167			self.assertEqual(0, val)
168			elif day_idx < 9:
169			self.assertEqual((day_idx - 1) + (field_idx * 10), val)
170			else:
171			self.assertEqual(7 + (field_idx * 10), val)
172			finally:
173			tempdir.cleanup()
174
175			def test_adjust_forward_fill_minute(self):
176			tempdir = TempDirectory()
177			try:
178			start_day = pd.Timestamp("2013-06-21", tz='UTC')
179			end_day = pd.Timestamp("2013-06-24", tz='UTC')
180
181			env = TradingEnvironment()
182			env.write_data(
183			equities_data={
184			0: {
185			'start_date': start_day,
186			'end_date': env.next_trading_day(end_day)
187			}
188			}
189			)
190
191			minutes = env.minutes_for_days_in_range(
192			start=start_day,
193			end=end_day
194			)
195
196			df = pd.DataFrame({
197			# 390 bars of real data, then 100 missing bars, then 290
198			# bars of data again
199			"open": np.array(list(range(0, 390)) + [0] * 100 +
200			list(range(390, 680))) * 1000,
201			"high": np.array(list(range(1000, 1390)) + [0] * 100 +
202			list(range(1390, 1680))) * 1000,
203			"low": np.array(list(range(2000, 2390)) + [0] * 100 +
204			list(range(2390, 2680))) * 1000,
205			"close": np.array(list(range(3000, 3390)) + [0] * 100 +
206			list(range(3390, 3680))) * 1000,
207			"volume": np.array(list(range(4000, 4390)) + [0] * 100 +
208			list(range(4390, 4680))),
209			"minute": minutes
210			})
211
212			MinuteBarWriterFromDataFrames(
213			pd.Timestamp('2002-01-02', tz='UTC')).write(
214			tempdir.path, {0: df})
215
216			sim_params = SimulationParameters(
217			period_start=minutes[0],
218			period_end=minutes[-1],
219			data_frequency="minute",
220			env=env
221			)
222
223			# create a split for 6/24
224			adjustments_path = os.path.join(tempdir.path, "adjustments.db")
225			writer = SQLiteAdjustmentWriter(adjustments_path,
226			pd.date_range(start=start_day,
227			end=end_day),
228			None)
229
230			splits = pd.DataFrame([{
231			'effective_date': int(end_day.value / 1e9),
232			'ratio': 0.5,
233			'sid': 0
234			}])
235
236			dividend_data = {
237			# Hackery to make the dtypes correct on an empty frame.
238			'ex_date': np.array([], dtype='datetime64[ns]'),
239			'pay_date': np.array([], dtype='datetime64[ns]'),
240			'record_date': np.array([], dtype='datetime64[ns]'),
241			'declared_date': np.array([], dtype='datetime64[ns]'),
242			'amount': np.array([], dtype=float),
243			'sid': np.array([], dtype=int),
244			}
245			dividends = pd.DataFrame(
246			dividend_data,
247			index=pd.DatetimeIndex([], tz='UTC'),
248			columns=['ex_date',
249			'pay_date',
250			'record_date',
251			'declared_date',
252			'amount',
253			'sid']
254			)
255
256			merger_data = {
257			# Hackery to make the dtypes correct on an empty frame.
258			'effective_date': np.array([], dtype=int),
259			'ratio': np.array([], dtype=float),
260			'sid': np.array([], dtype=int),
261			}
262			mergers = pd.DataFrame(
263			merger_data,
264			index=pd.DatetimeIndex([], tz='UTC')
265			)
266
267			writer.write(splits, mergers, dividends)
268
269			equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
270
271			dp = DataPortal(
272			env,
273			equity_minute_reader=equity_minute_reader,
274			adjustment_reader=SQLiteAdjustmentReader(adjustments_path)
275			)
276
277			# phew, finally ready to start testing.
278			for idx, minute in enumerate(minutes[:390]):
279			for field_idx, field in enumerate(["open", "high", "low",
280			"close", "volume"]):
281			self.assertEqual(
282			dp.get_spot_value(
283			0, field,
284			dt=minute,
285			data_frequency=sim_params.data_frequency),
286			idx + (1000 * field_idx)
287			)
288
289			for idx, minute in enumerate(minutes[390:490]):
290			# no actual data for this part, so we'll forward-fill.
291			# make sure the forward-filled values are adjusted.
292			for field_idx, field in enumerate(["open", "high", "low",
293			"close"]):
294			self.assertEqual(
295			dp.get_spot_value(
296			0, field,
297			dt=minute,
298			data_frequency=sim_params.data_frequency),
299			(389 + (1000 * field_idx)) / 2.0
300			)
301
302			self.assertEqual(
303			dp.get_spot_value(
304			0, "volume",
305			dt=minute,
306			data_frequency=sim_params.data_frequency),
307			8778 # 4389 * 2
308			)
309
310			for idx, minute in enumerate(minutes[490:]):
311			# back to real data
312			for field_idx, field in enumerate(["open", "high", "low",
313			"close", "volume"]):
314			self.assertEqual(
315			dp.get_spot_value(
316			0, field,
317			dt=minute,
318			data_frequency=sim_params.data_frequency
319			),
320			(390 + idx + (1000 * field_idx))
321			)
322			finally:
323			tempdir.cleanup()
324
325			def test_spot_value_futures(self):
326			tempdir = TempDirectory()
327			try:
328			start_dt = pd.Timestamp("2015-11-20 20:11", tz='UTC')
329			end_dt = pd.Timestamp(start_dt + timedelta(minutes=10000))
330
331			zeroes_buffer = \
332			[0] * int((start_dt -
333			normalize_date(start_dt)).total_seconds() / 60)
334
335			df = pd.DataFrame({
336			"open": np.array(zeroes_buffer + list(range(0, 10000))) * 1000,
337			"high": np.array(
338			zeroes_buffer + list(range(10000, 20000))) * 1000,
339			"low": np.array(
340			zeroes_buffer + list(range(20000, 30000))) * 1000,
341			"close": np.array(
342			zeroes_buffer + list(range(30000, 40000))) * 1000,
343			"volume": np.array(zeroes_buffer + list(range(40000, 50000)))
344			})
345
346			path = os.path.join(tempdir.path, "123.bcolz")
347			ctable = bcolz.ctable.fromdataframe(df, rootdir=path)
348			ctable.attrs["start_dt"] = start_dt.value / 1e9
349			ctable.attrs["last_dt"] = end_dt.value / 1e9
350
351			env = TradingEnvironment()
352			env.write_data(futures_data={
353			123: {
354			"start_date": normalize_date(start_dt),
355			"end_date": env.next_trading_day(normalize_date(end_dt)),
356			'symbol': 'TEST_FUTURE',
357			'asset_type': 'future',
358			}
359			})
360
361			future_minute_reader = FutureMinuteReader(tempdir.path)
362
363			dp = DataPortal(
364			env,
365			future_minute_reader=future_minute_reader
366			)
367
368			future123 = env.asset_finder.retrieve_asset(123)
369
370			data_frequency = 'minute'
371
372			for i in range(0, 10000):
373			dt = pd.Timestamp(start_dt + timedelta(minutes=i))
374
375			self.assertEqual(i,
376			dp.get_spot_value(
377			future123, "open", dt, data_frequency))
378			self.assertEqual(i + 10000,
379			dp.get_spot_value(
380			future123, "high", dt, data_frequency))
381			self.assertEqual(i + 20000,
382			dp.get_spot_value(
383			future123, "low", dt, data_frequency))
384			self.assertEqual(i + 30000,
385			dp.get_spot_value(
386			future123, "close", dt, data_frequency))
387			self.assertEqual(i + 40000,
388			dp.get_spot_value(
389			future123, "volume", dt, data_frequency))
390
391			finally:
392			tempdir.cleanup()
393

quantopian / zipline

Pull Request — master (#858)

test_adjust_forward_fill_minute() C

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